Parameter estimation for personalization of liver tumor radiofrequency ablation

Chloè Audigier, Tommaso Mansi, Hervè Delingette, Saikiran Rapaka, Viorel Mihalef, Daniel Carnegie, Emad Boctor, Michael Choti, Ali Kamen, Dorin Comaniciu, Nicholas Ayache

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mathematical modeling has the potential to assist radiofrequency ablation (RFA) of tumors as it enables prediction of the extent of ablation. However, the accuracy of the simulation is challenged by the material properties since they are patient-specific, temperature and space dependent. In this paper, we present a framework for patientspecific radiofrequency ablation modeling of multiple lesions in the case of metastatic diseases. The proposed forward model is based upon a computational model of heat diffusion, cellular necrosis and blood flow through vessels and liver which relies on patient images. We estimate the most sensitive material parameters, those need to be personalized from the available clinical imaging and data. The selected parameters are then estimated using inverse modeling such that the point-to-mesh distance between the computed necrotic area and observed lesions is minimized. Based on the personalized parameters, the ablation of the remaining lesions are predicted. The framework is applied to a dataset of seven lesions from three patients including pre- and post-operative CT images. In each case, the parameters were estimated on one tumor and RFA is simulated on the other tumor(s) using these personalized parameters, assuming the parameters to be spatially invariant within the same patient. Results showed significantly good correlation between predicted and actual ablation extent (average point-to-mesh errors of 4.03 mm).

Original languageEnglish (US)
Title of host publicationAbdominal Imaging
Subtitle of host publicationComputational and Clinical Applications - 6th International Workshop, ABDI 2014 held in conjunction with MICCAI 2014
EditorsHiroyuki Yoshida, Janne J. Näppi, Sanjay Saini
PublisherSpringer Verlag
Pages3-12
Number of pages10
ISBN (Electronic)9783319136912
DOIs
StatePublished - Jan 1 2014
Event6th International Workshop on Abdominal Imaging: Computational and Clinical Applications, ABDI 2014 held in conjunction with 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Cambridge, United States
Duration: Sep 14 2014Sep 14 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8676
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Workshop on Abdominal Imaging: Computational and Clinical Applications, ABDI 2014 held in conjunction with 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityCambridge
Period9/14/149/14/14

Keywords

  • Heat diffusion
  • Inverse modeling
  • Liver
  • Radiofrequency ablation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Audigier, C., Mansi, T., Delingette, H., Rapaka, S., Mihalef, V., Carnegie, D., Boctor, E., Choti, M., Kamen, A., Comaniciu, D., & Ayache, N. (2014). Parameter estimation for personalization of liver tumor radiofrequency ablation. In H. Yoshida, J. J. Näppi, & S. Saini (Eds.), Abdominal Imaging: Computational and Clinical Applications - 6th International Workshop, ABDI 2014 held in conjunction with MICCAI 2014 (pp. 3-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8676). Springer Verlag. https://doi.org/10.1007/978-3-319-13692-9_1